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Known public company plus common Snowflake/dbt/Python stack and metro hiring drive moderate competition.
Core data engineering skills are transferable, but SaaS marketing attribution and Snowflake specialization increase domain fit importance.
Explicit 8+ years, 2+ years management, and mandatory Snowflake/dbt/Python/infra skills create strict shortlisting.
Lead and mentor a team of data engineers to develop and maintain scalable data pipelines, data models, and analytics infrastructure using Snowflake, dbt, Python, Terraform, and Airflow.
Design and build advanced marketing attribution models (e.g., Markov Chain with time-decay) to quantify multi-touch channel contribution for SaaS marketing and sales funnel, influencing budget allocation decisions.
Drive innovation, best practices adoption, AI-assisted engineering tools implementation, and manage communication and delivery of the team's projects to stakeholders.
Bachelor's degree in Computer Science, Engineering, or related field.
8+ years of experience in data engineering with deep SQL knowledge.
2+ years of people management experience with direct technical reports.
Experience requirements: 5+ years data warehousing concepts and technologies; 4+ years with Git and Snowflake; 3+ years with dbt; working knowledge of probabilistic attribution modeling for SaaS marketing funnels.
Experienced leader able to mentor and grow data engineering teams while delivering complex data solutions and analytics products.
Strong technical expertise with modern data stack technologies (Snowflake, dbt, Terraform, Airflow) and cloud infrastructure as code.
Strategic thinker who can translate complex marketing and sales data models into scalable production data pipelines and reporting to drive business decisions.